---
title: "COVID-19 employment shock and unemployment benefit number"
author: ""
date: "`r Sys.Date()`"
output:
 html_document:
    css : R_style.css
    # theme: cosmo
    highlight: haddock     # Rスクリプトのハイライト形式
    code_folding: 'hide'  # Rコードの折りたたみ表示を設定
    toc: TRUE
    toc_depth: 3           # 見出しの表示とその深さを指定
    toc_float: true        # 見出しを横に表示し続ける
    number_sections: true # 見出しごとに番号を振る
    df_print: paged        # head()の出力をnotebook的なものに（tibbleと相性良）
    latex_engine: xelatex  # zxjatypeパッケージを使用するために変更
    # fig_height: 4.5          # 画像サイズのデフォルトを設定
    # fig_width: 8           # 画像サイズのデフォルトを設定
    dev: png
classoption: xelatex,ja=standard
editor_options: 
  chunk_output_type: console
---
# Rmd Settings

```{r setup, include=FALSE}
Sys.setenv(LANG = "en") #English
knitr::opts_chunk$set(echo = TRUE)
```


```{r, include=FALSE}
rm(list = ls())

path <- getwd()

setwd(path)

# packages
pacman::p_load(tidyverse, plotly,readxl,scales, extrafont,PerformanceAnalytics, GGally, patchwork, ggpubr, DT, estimatr, texreg,　modelsummary)

# Font for windows and mac
if (stringr::str_detect(path, pattern="D:")){ 
  
  theme_set(theme_gray(base_size = 10, base_family = "Arial"))        # For Windows

 } else{
  
  theme_set(theme_gray(base_size = 10, base_family = "HiraginoSans-W3"))  # For Mac OS

 }
```

# Contents
covid_on_unemp_benefit_numberのOLSとWLS

# Read functions/関数の読み込み
```{r}
source("functions.R")
```


# Read data/分析用データの読み込み
```{r}
df_analysis <- readr::read_csv("output/df_analysis.csv")
```

# Y = total unemployment benefit recipients/男女合計の雇用保険受給者数
## OLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_notrend(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates  <- DID_coefficients_main(DID_results = estimation_results, 
                                 treat_var = "job_seeker_total_shock",
                                 estimation_label = "total_OLS_notrend")

# Event study graph
graph_total_OLS_notrend <- event_study_graph(data = df_estimates ,
                                          graph_title = "total_OLS_notrend")

graph_total_OLS_notrend

estimates_total_OLS_notrend <- df_estimates 　 #for robustness check
```

## WLS, no trends

```{r}
df_analysis$population_total

# DID estimation
estimation_results <- dynamic_DID_WLS_notrend(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "total_WLS_notrend")

# Event study graph
graph_total_WLS_notrend <- event_study_graph(data = df_estimates,
                                          graph_title = "total_WLS_notrend")

graph_total_WLS_notrend


estimates_total_WLS_notrend <- df_estimates 　 #for robustness check
```

## OLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_trend(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "total_OLS_trend")

# Event study graph
graph_total_OLS_trend <- event_study_graph(data = df_estimates,
                                          graph_title = "total_OLS_trend")

graph_total_OLS_trend

estimates_total_OLS_trend <- df_estimates 　 #for robustness check
```

## WLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_trend(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "total_WLS_trend")

# Event study graph
graph_total_WLS_trend <- event_study_graph(data = df_estimates,
                                          graph_title = "total_WLS_trend")

graph_total_WLS_trend

estimates_total_WLS_trend <- df_estimates 　 #for robustness check
```







# Y = total unemployment benefit recipients/男女合計の雇用保険受給者数 with covar
## OLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_notrend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates　<- DID_coefficients_main(DID_results = estimation_results, 
                                 treat_var = "job_seeker_total_shock",
                                 estimation_label = "total_OLS_notrend")

# Event study graph
graph_total_OLS_notrend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "total_OLS_notrend")

graph_total_OLS_notrend_covar

estimates_total_OLS_notrend_covar  <- df_estimates 　 #for robustness check
```

## WLS, no trends

```{r}
df_analysis$population_total

# DID estimation
estimation_results <- dynamic_DID_WLS_notrend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "total_WLS_notrend")


# Event study graph
graph_total_WLS_notrend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "total_WLS_notrend")

graph_total_WLS_notrend_covar

estimates_total_WLS_notrend_covar  <- df_estimates 　 #for robustness check
```

## OLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_trend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "total_OLS_trend")

# Event study graph
graph_total_OLS_trend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "total_OLS_trend")

graph_total_OLS_trend_covar

estimates_total_OLS_trend_covar  <- df_estimates 　 #for robustness check
```

## WLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "total_WLS_trend")

# Event study graph
graph_total_WLS_trend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "total_WLS_trend")

graph_total_WLS_trend_covar

estimates_total_WLS_trend_covar  <- df_estimates 　 #for robustness check
```




# Y = total unemployment benefit recipients(YOY)/男女合計の雇用保険受給者数(前年同月差）

## OLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_notrend(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_total_OLS_notrend")

# Event study graph
graph_yoy_total_OLS_notrend <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_total_OLS_notrend")

graph_yoy_total_OLS_notrend

estimates_yoy_total_OLS_notrend<- df_estimates 　 #for robustness check
```

## WLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_notrend(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_total_WLS_notrend")

# Event study graph
graph_yoy_total_WLS_notrend <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_total_WLS_notrend")

graph_yoy_total_WLS_notrend

estimates_yoy_total_WLS_notrend <- df_estimates 　 #for robustness check
```

## OLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_trend(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_total_OLS_trend")

# Event study graph
graph_yoy_total_OLS_trend <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_total_OLS_trend")

graph_yoy_total_OLS_trend

estimates_yoy_total_OLS_trend <- df_estimates 　 #for robustness check
```

## WLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_trend(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates  <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_total_WLS_trend")

# Event study graph
graph_yoy_total_WLS_trend <- event_study_graph(data = df_estimates ,
                                          graph_title = "yoy_total_WLS_trend")

graph_yoy_total_WLS_trend

estimates_yoy_total_WLS_trend <- df_estimates 　 #for robustness check

results_yot_total_WLS_trend <- estimation_results # for only-post DID table
```

## WLS, with trends, post-covid-month dummies
```{r}
# DID estimation
estimation_results <- dynamic_onlypost_DID_WLS_trend(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_total_WLS_trend")

# Event study graph
graph_yoy_total_WLS_trend_onlypost <- event_study_graph(data = df_estimates ,
                                          graph_title = "yoy_total_WLS_trend")

ggplotly(graph_yoy_total_WLS_trend_onlypost)

estimates_yoy_total_WLS_trend_onlypost <- df_estimates #for robustness check

results_yot_total_WLS_trend_onlypost <- estimation_results # for only-post DID table
```

# Y = total unemployment benefit recipients(YOY)/男女合計の雇用保険受給者数(前年同月差）with covar
## OLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_notrend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_total_OLS_notrend")

# Event study graph
graph_yoy_total_OLS_notrend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_total_OLS_notrend")

graph_yoy_total_OLS_notrend_covar

estimates_yoy_total_OLS_notrend_covar  <- df_estimates 　 #for robustness check
```

## WLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_notrend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_total_WLS_notrend")

# Event study graph
graph_yoy_total_WLS_notrend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_total_WLS_notrend")

graph_yoy_total_WLS_notrend_covar

estimates_yoy_total_WLS_notrend_covar  <- df_estimates 　 #for robustness check
```

## OLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_trend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_total_OLS_trend")

# Event study graph
graph_yoy_total_OLS_trend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_total_OLS_trend")

graph_yoy_total_OLS_trend_covar

estimates_yoy_total_OLS_trend_covar  <- df_estimates 　 #for robustness check
```

## WLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_total_WLS_trend")

# Event study graph
graph_yoy_total_WLS_trend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_total_WLS_trend")

graph_yoy_total_WLS_trend_covar

estimates_yoy_total_WLS_trend_covar  <- df_estimates 　 #for robustness check

results_yot_total_WLS_trend_covar <- estimation_results # for only-post DID table
```

## WLS, with trends, post-covid-month dummies
```{r}
# DID estimation
estimation_results <- dynamic_onlypost_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_total, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_total_WLS_trend")

# Event study graph
graph_yoy_total_WLS_trend_covar_onlypost <- event_study_graph(data = df_estimates ,
                                          graph_title = "yoy_total_WLS_trend")

ggplotly(graph_yoy_total_WLS_trend_covar_onlypost)

estimates_yoy_total_WLS_trend_covar_onlypost <- df_estimates #for robustness check

results_yot_total_WLS_trend_covar_onlypost <- estimation_results # for only-post DID table
```




# Y = female unemployment benefit recipients/女性の雇用保険受給者数

## OLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_notrend(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                 treat_var = "job_seeker_total_shock",
                                 estimation_label = "female_OLS_notrend")

# Event study graph
graph_female_OLS_notrend <- event_study_graph(data = df_estimates,
                                          graph_title = "female_OLS_notrend")

graph_female_OLS_notrend

estimates_female_OLS_notrend <- df_estimates 　 #for robustness check
```

## WLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_notrend(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates<- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "female_WLS_notrend")

# Event study graph
graph_female_WLS_notrend <- event_study_graph(data = df_estimates,
                                          graph_title = "female_WLS_notrend")

graph_female_WLS_notrend

estimates_female_WLS_notrend <- df_estimates 　 #for robustness check
```

## OLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_trend(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "female_OLS_trend")

# Event study graph
graph_female_OLS_trend <- event_study_graph(data = df_estimates,
                                          graph_title = "female_OLS_trend")

graph_female_OLS_trend

estimates_female_OLS_trend <- df_estimates 　 #for robustness check
```

## WLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_trend(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "female_WLS_trend")

# Event study graph
graph_female_WLS_trend <- event_study_graph(data = df_estimates,
                                          graph_title = "female_WLS_trend")

graph_female_WLS_trend

estimates_female_WLS_trend <- df_estimates 　 #for robustness check
```




# Y = female unemployment benefit recipients/女性の雇用保険受給者数 with covar
## OLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_notrend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                 treat_var = "job_seeker_total_shock",
                                 estimation_label = "female_OLS_notrend")

# Event study graph
graph_female_OLS_notrend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "female_OLS_notrend")

graph_female_OLS_notrend_covar

estimates_female_OLS_notrend_covar  <- df_estimates 　 #for robustness check
```

## WLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_notrend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "female_WLS_notrend")

# Event study graph
graph_female_WLS_notrend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "female_WLS_notrend")

graph_female_WLS_notrend_covar

estimates_female_WLS_notrend_covar  <- df_estimates 　 #for robustness check
```

## OLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_trend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "female_OLS_trend")

# Event study graph
graph_female_OLS_trend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "female_OLS_trend")

graph_female_OLS_trend_covar

estimates_female_OLS_trend_covar  <- df_estimates 　 #for robustness check
```

## WLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "female_WLS_trend")

# Event study graph
graph_female_WLS_trend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "female_WLS_trend")

graph_female_WLS_trend_covar

estimates_female_WLS_trend_covar  <- df_estimates 　 #for robustness check
```



# Y = female unemployment benefit recipients(YOY)/女性の雇用保険受給者数(前年同月差）

## OLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_notrend(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_female_OLS_notrend")

# Event study graph
graph_yoy_female_OLS_notrend <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_female_OLS_notrend")

graph_yoy_female_OLS_notrend

estimates_yoy_female_OLS_notrend <- df_estimates 　 #for robustness check
```

## WLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_notrend(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_female_WLS_notrend")

# Event study graph
graph_yoy_female_WLS_notrend <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_female_WLS_notrend")

graph_yoy_female_WLS_notrend

estimates_yoy_female_WLS_notrend <- df_estimates 　 #for robustness check
```

## OLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_trend(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_female_OLS_trend")

# Event study graph
graph_yoy_female_OLS_trend <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_female_OLS_trend")

graph_yoy_female_OLS_trend

estimates_yoy_female_OLS_trend <- df_estimates 　 #for robustness check
```

## WLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_trend(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_female_WLS_trend")

# Event study graph
graph_yoy_female_WLS_trend <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_female_WLS_trend")

graph_yoy_female_WLS_trend

estimates_yoy_female_WLS_trend <- df_estimates 　 #for robustness check

results_yot_female_WLS_trend <- estimation_results # for only-post DID table
```

## WLS, with trends, post-covid-month dummies
```{r}
# DID estimation
estimation_results <- dynamic_onlypost_DID_WLS_trend(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_female_WLS_trend")

# Event study graph
graph_yoy_female_WLS_trend_onlypost <- event_study_graph(data = df_estimates ,
                                          graph_title = "yoy_female_WLS_trend")

ggplotly(graph_yoy_female_WLS_trend_onlypost)

estimates_yoy_female_WLS_trend_onlypost <- df_estimates #for robustness check

results_yot_female_WLS_trend_onlypost <- estimation_results # for only-post DID table
```


# Y = female unemployment benefit recipients(YOY)/女性の雇用保険受給者数(前年同月差）with covar
## OLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_notrend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_female_OLS_notrend")

# Event study graph
graph_yoy_female_OLS_notrend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_female_OLS_notrend")

graph_yoy_female_OLS_notrend_covar

estimates_yoy_female_OLS_notrend_covar  <- df_estimates 　 #for robustness check
```

## WLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_notrend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_female_WLS_notrend")

# Event study graph
graph_yoy_female_WLS_notrend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_female_WLS_notrend")

graph_yoy_female_WLS_notrend_covar

estimates_yoy_female_WLS_notrend_covar  <- df_estimates 　 #for robustness check
```

## OLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_trend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_female_OLS_trend")

# Event study graph
graph_yoy_female_OLS_trend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_female_OLS_trend")

graph_yoy_female_OLS_trend_covar

estimates_yoy_female_OLS_trend_covar   <- df_estimates 　 #for robustness check
```

## WLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_female_WLS_trend")

# Event study graph
graph_yoy_female_WLS_trend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_female_WLS_trend")

graph_yoy_female_WLS_trend_covar

estimates_yoy_female_WLS_trend_covar  <- df_estimates 　 #for robustness check

results_yot_female_WLS_trend_covar <- estimation_results # for only-post DID table
```



## WLS, with trends, post-covid-month dummies
```{r}
# DID estimation
estimation_results <- dynamic_onlypost_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_female, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_female_WLS_trend")

# Event study graph
graph_yoy_female_WLS_trend_covar_onlypost <- event_study_graph(data = df_estimates ,
                                          graph_title = "yoy_female_WLS_trend")

ggplotly(graph_yoy_female_WLS_trend_covar_onlypost)

estimates_yoy_female_WLS_trend_covar_onlypost <- df_estimates #for robustness check

results_yot_female_WLS_trend_covar_onlypost <- estimation_results # for only-post DID table
```


# Y = male unemployment benefit recipients/男性の雇用保険受給者数

## OLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_notrend(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates<- DID_coefficients_main(DID_results = estimation_results, 
                                 treat_var = "job_seeker_total_shock",
                                 estimation_label = "male_OLS_notrend")

# Event study graph
graph_male_OLS_notrend <- event_study_graph(data = df_estimates,
                                          graph_title = "male_OLS_notrend")

graph_male_OLS_notrend

estimates_male_OLS_notrend <- df_estimates 　 #for robustness check
```

## WLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_notrend(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "male_WLS_notrend")

# Event study graph
graph_male_WLS_notrend <- event_study_graph(data = df_estimates,
                                          graph_title = "male_WLS_notrend")

graph_male_WLS_notrend

estimates_male_WLS_notrend <- df_estimates 　 #for robustness check
```

## OLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_trend(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "male_OLS_trend")

# Event study graph
graph_male_OLS_trend <- event_study_graph(data = df_estimates,
                                          graph_title = "male_OLS_trend")

graph_male_OLS_trend

estimates_male_OLS_trend <- df_estimates 　 #for robustness check
```

## WLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_trend(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "male_WLS_trend")

# Event study graph
graph_male_WLS_trend <- event_study_graph(data = df_estimates,
                                          graph_title = "male_WLS_trend")

graph_male_WLS_trend

estimates_male_WLS_trend  <- df_estimates 　 #for robustness check
```



# Y = male unemployment benefit recipients/男性の雇用保険受給者数 with covar
## OLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_notrend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                 treat_var = "job_seeker_total_shock",
                                 estimation_label = "male_OLS_notrend")

# Event study graph
graph_male_OLS_notrend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "male_OLS_notrend")

graph_male_OLS_notrend_covar

estimates_male_OLS_notrend_covar  <- df_estimates 　 #for robustness check
```

## WLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_notrend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "male_WLS_notrend")

# Event study graph
graph_male_WLS_notrend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "male_WLS_notrend")

graph_male_WLS_notrend_covar

estimates_male_WLS_notrend_covar  <- df_estimates 　 #for robustness check
```

## OLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_trend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "male_OLS_trend")

# Event study graph
graph_male_OLS_trend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "male_OLS_trend")

graph_male_OLS_trend_covar

estimates_male_OLS_trend_covar  <- df_estimates 　 #for robustness check
```

## WLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "male_WLS_trend")

# Event study graph
graph_male_WLS_trend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "male_WLS_trend")

graph_male_WLS_trend_covar

estimates_male_WLS_trend_covar  <- df_estimates 　 #for robustness check
```




# Y = male unemployment benefit recipients(YOY)/男性の雇用保険受給者数(前年同月差）

## OLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_notrend(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_male_OLS_notrend")

# Event study graph
graph_yoy_male_OLS_notrend <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_male_OLS_notrend")

graph_yoy_male_OLS_notrend

estimates_yoy_male_OLS_notrend  <- df_estimates 　 #for robustness check
```

## WLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_notrend(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_male_WLS_notrend")

# Event study graph
graph_yoy_male_WLS_notrend <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_male_WLS_notrend")

graph_yoy_male_WLS_notrend

estimates_yoy_male_WLS_notrend  <- df_estimates 　 #for robustness check
```

## OLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_trend(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_male_OLS_trend")

# Event study graph
graph_yoy_male_OLS_trend <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_male_OLS_trend")

graph_yoy_male_OLS_trend

estimates_yoy_male_OLS_trend  <- df_estimates 　 #for robustness check
```

## WLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_trend(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_male_WLS_trend")

# Event study graph
graph_yoy_male_WLS_trend <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_male_WLS_trend")

graph_yoy_male_WLS_trend

estimates_yoy_male_WLS_trend  <- df_estimates 　 #for robustness check

results_yot_male_WLS_trend <- estimation_results # for only-post DID table
```

## WLS, with trends, post-covid-month dummies
```{r}
# DID estimation
estimation_results <- dynamic_onlypost_DID_WLS_trend(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_male_WLS_trend")

# Event study graph
graph_yoy_male_WLS_trend_onlypost <- event_study_graph(data = df_estimates ,
                                          graph_title = "yoy_male_WLS_trend")

ggplotly(graph_yoy_male_WLS_trend_onlypost)

estimates_yoy_male_WLS_trend_onlypost <- df_estimates #for robustness check

results_yot_male_WLS_trend_onlypost <- estimation_results # for only-post DID table
```

# Y = male unemployment benefit recipients(YOY)/男性の雇用保険受給者数(前年同月差）with covar
## OLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_notrend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_male_OLS_notrend")

# Event study graph
graph_yoy_male_OLS_notrend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_male_OLS_notrend")

graph_yoy_male_OLS_notrend_covar

estimates_yoy_male_OLS_notrend_covar  <- df_estimates 　 #for robustness check
```

## WLS, no trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_notrend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_male_WLS_notrend")

# Event study graph
graph_yoy_male_WLS_notrend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_male_WLS_notrend")

graph_yoy_male_WLS_notrend_covar

estimates_yoy_male_WLS_notrend_covar  <- df_estimates 　 #for robustness check
```

## OLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_OLS_trend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_male_OLS_trend")

# Event study graph
graph_yoy_male_OLS_trend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_male_OLS_trend")

graph_yoy_male_OLS_trend_covar

estimates_yoy_male_OLS_trend_covar  <- df_estimates 　 #for robustness check
```

## WLS, with trends

```{r}
# DID estimation
estimation_results <- dynamic_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_male_WLS_trend")

# Event study graph
graph_yoy_male_WLS_trend_covar <- event_study_graph(data = df_estimates,
                                          graph_title = "yoy_male_WLS_trend")

graph_yoy_male_WLS_trend_covar

estimates_yoy_male_WLS_trend_covar  <- df_estimates 　 #for robustness check

results_yot_male_WLS_trend_covar <- estimation_results # for only-post DID table
```

## WLS, with trends, post-covid-month dummies
```{r}
# DID estimation
estimation_results <- dynamic_onlypost_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$yoy_unemp_benefit_number_male, 
                    treat_var = df_analysis$job_seeker_total_shock)

#texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3,)

# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "job_seeker_total_shock",
                                 estimation_label = "yoy_male_WLS_trend")

# Event study graph
graph_yoy_male_WLS_trend_covar_onlypost <- event_study_graph(data = df_estimates ,
                                          graph_title = "yoy_male_WLS_trend")

ggplotly(graph_yoy_male_WLS_trend_covar_onlypost)

estimates_yoy_male_WLS_trend_covar_onlypost <- df_estimates #for robustness check

results_yot_male_WLS_trend_covar_onlypost <- estimation_results # for only-post DID table
```


# Merge outcome results/アウトカム結果の結合
## Y = total unemployment benefit recipients/男女合計の雇用保険受給者数
```{r}
#merge and label estimates data
estimates_total_bind <- dplyr::bind_rows(estimates_total_OLS_notrend, 
                                         estimates_total_WLS_notrend, 
                                         estimates_total_OLS_trend,
                                         estimates_total_WLS_trend)

#change labels and reorder labels
estimates_total_bind <- estimates_labeling_main(estimates_total_bind)

# Display results
DT::datatable(estimates_total_bind) %>%
   DT::formatRound(columns = c("estimate", "ci_lower", "ci_upper"), digits=3)

#graph
graph_total_bind <- event_study_graph_bind_main(data = estimates_total_bind, 
                                             graph_title = "Total")

ggplotly(graph_total_bind)
```

## Y = total unemployment benefit recipients/男女合計の雇用保険受給者数 with covar
```{r}
#merge and label estimates data
estimates_total_bind <- dplyr::bind_rows(estimates_total_OLS_notrend_covar, 
                                         estimates_total_WLS_notrend_covar, 
                                         estimates_total_OLS_trend_covar,
                                         estimates_total_WLS_trend_covar)

#change labels and reorder labels
estimates_total_bind <- estimates_labeling_main(estimates_total_bind)

# Display results
DT::datatable(estimates_total_bind) %>%
   DT::formatRound(columns = c("estimate", "ci_lower", "ci_upper"), digits=3)

#graph
graph_total_bind_covar <- event_study_graph_bind_main(data = estimates_total_bind, 
                                             graph_title = "Total, with covar")

ggplotly(graph_total_bind_covar)
```

## Y = total unemployment benefit(YOY) recipients/男女合計の雇用保険受給者数(対前年同期差)
```{r}
#merge and label estimates data
estimates_yoy_total_bind <- dplyr::bind_rows(estimates_yoy_total_OLS_notrend, 
                                             estimates_yoy_total_WLS_notrend, 
                                             estimates_yoy_total_OLS_trend,
                                             estimates_yoy_total_WLS_trend)

#change labels and reorder labels
estimates_yoy_total_bind <- estimates_labeling_main(estimates_yoy_total_bind)

# display results
DT::datatable(estimates_yoy_total_bind) %>%
   DT::formatRound(columns = c("estimate", "ci_lower", "ci_upper"), digits=3)

#graph
graph_yoy_total_bind <- event_study_graph_bind_main(data = estimates_yoy_total_bind, 
                                             graph_title = "Total, YOY")

ggplotly(graph_yoy_total_bind)
```

## Y = total unemployment benefit recipients(YOY)/男女合計の雇用保険受給者数(対前年同期差) with covar
```{r}
#merge and label estimates data
estimates_yoy_total_bind <- dplyr::bind_rows(estimates_yoy_total_OLS_notrend_covar, 
                                             estimates_yoy_total_WLS_notrend_covar, 
                                             estimates_yoy_total_OLS_trend_covar,
                                             estimates_yoy_total_WLS_trend_covar)

#change labels and reorder labels
estimates_yoy_total_bind <- estimates_labeling_main(estimates_yoy_total_bind)

# display results
DT::datatable(estimates_yoy_total_bind) %>%
   DT::formatRound(columns = c("estimate", "ci_lower", "ci_upper"), digits=3)

#graph
graph_yoy_total_bind_covar <- event_study_graph_bind_main(data = estimates_yoy_total_bind, 
                                             graph_title = "Total, YOY, with covar")

ggplotly(graph_yoy_total_bind_covar)

```


## Y = female unemployment benefit recipients/女性の雇用保険受給者数

```{r}
#merge and label estimates data
estimates_female_bind <- dplyr::bind_rows(estimates_female_OLS_notrend, 
                                          estimates_female_WLS_notrend, 
                                          estimates_female_OLS_trend,
                                          estimates_female_WLS_trend)

#change labels and reorder labels
estimates_female_bind <- estimates_labeling_main(estimates_female_bind)

# display results
DT::datatable(estimates_female_bind) %>%
   DT::formatRound(columns = c("estimate", "ci_lower", "ci_upper"), digits=3)

#graph
graph_female_bind <- event_study_graph_bind_main(data = estimates_female_bind, 
                                             graph_title = "Female")

ggplotly(graph_female_bind)

```

## Y = female unemployment benefit recipients/女性の雇用保険受給者数 with covar
```{r}
#merge and label estimates data
estimates_female_bind <- dplyr::bind_rows(estimates_female_OLS_notrend_covar, 
                                          estimates_female_WLS_notrend_covar, 
                                          estimates_female_OLS_trend_covar,
                                          estimates_female_WLS_trend_covar)

#change labels and reorder labels
estimates_female_bind <- estimates_labeling_main(estimates_female_bind)

# display results
DT::datatable(estimates_female_bind) %>%
   DT::formatRound(columns = c("estimate", "ci_lower", "ci_upper"), digits=3)

#graph
graph_female_bind_covar <- event_study_graph_bind_main(data = estimates_female_bind, 
                                             graph_title = "Female, with covar")

ggplotly(graph_female_bind_covar)

```


## Y = female unemployment benefit recipients(YOY)/女性の雇用保険受給者数(対前年同期差)

```{r}
#merge and label estimates data
estimates_yoy_female_bind <- dplyr::bind_rows(estimates_yoy_female_OLS_notrend, 
                                              estimates_yoy_female_WLS_notrend, 
                                              estimates_yoy_female_OLS_trend,
                                              estimates_yoy_female_WLS_trend)

#change labels and reorder labels
estimates_yoy_female_bind <- estimates_labeling_main(estimates_yoy_female_bind)


# display results
DT::datatable(estimates_yoy_female_bind) %>%
   DT::formatRound(columns = c("estimate", "ci_lower", "ci_upper"), digits=3)

#graph
graph_yoy_female_bind <- event_study_graph_bind_main(data = estimates_yoy_female_bind, 
                                             graph_title = "Female, YOY")

ggplotly(graph_yoy_female_bind)

```

## Y = female unemployment benefit recipients(YOY)/女性の雇用保険受給者数(対前年同期差) with covar

```{r}
#merge and label estimates data
estimates_yoy_female_bind <- dplyr::bind_rows(estimates_yoy_female_OLS_notrend_covar, 
                                              estimates_yoy_female_WLS_notrend_covar, 
                                              estimates_yoy_female_OLS_trend_covar,
                                              estimates_yoy_female_WLS_trend_covar)

#change labels and reorder labels
estimates_yoy_female_bind <- estimates_labeling_main(estimates_yoy_female_bind)


# display results
DT::datatable(estimates_yoy_female_bind) %>%
   DT::formatRound(columns = c("estimate", "ci_lower", "ci_upper"), digits=3)

#graph
graph_yoy_female_bind_covar <- event_study_graph_bind_main(data = estimates_yoy_female_bind, 
                                             graph_title = "Female, YOY, with covar")

ggplotly(graph_yoy_female_bind_covar)

```

## Y = male unemployment benefit recipients/男性の雇用保険受給者数

```{r}
#merge and label estimates data
estimates_male_bind <- dplyr::bind_rows(estimates_male_OLS_notrend, 
                                        estimates_male_WLS_notrend, 
                                        estimates_male_OLS_trend,
                                        estimates_male_WLS_trend)

#change labels and reorder labels
estimates_male_bind <- estimates_labeling_main(estimates_male_bind)


# display results
DT::datatable(estimates_male_bind) %>%
   DT::formatRound(columns = c("estimate", "ci_lower", "ci_upper"), digits=3)

#graph
graph_male_bind <- event_study_graph_bind_main(data = estimates_male_bind, 
                                             graph_title = "Male")

ggplotly(graph_male_bind)

```

## Y = male unemployment benefit recipients/男性の雇用保険受給者数 with covar

```{r}
#merge and label estimates data
estimates_male_bind <- dplyr::bind_rows(estimates_male_OLS_notrend_covar, 
                                        estimates_male_WLS_notrend_covar, 
                                        estimates_male_OLS_trend_covar,
                                        estimates_male_WLS_trend_covar)

#change labels and reorder labels
estimates_male_bind <- estimates_labeling_main(estimates_male_bind)


# display results
DT::datatable(estimates_male_bind) %>%
   DT::formatRound(columns = c("estimate", "ci_lower", "ci_upper"), digits=3)

#graph
graph_male_bind_covar <- event_study_graph_bind_main(data = estimates_male_bind, 
                                             graph_title = "Male, with covar")

ggplotly(graph_male_bind_covar)

```

## Y = male unemployment benefit recipients(YOY)/男性の雇用保険受給者数(対前年同期差)

```{r}
#merge and label estimates data
estimates_yoy_male_bind <- dplyr::bind_rows(estimates_yoy_male_OLS_notrend, 
                                            estimates_yoy_male_WLS_notrend, 
                                            estimates_yoy_male_OLS_trend,
                                            estimates_yoy_male_WLS_trend)

#change labels and reorder labels
estimates_yoy_male_bind <- estimates_labeling_main(estimates_yoy_male_bind)


# display results
DT::datatable(estimates_yoy_male_bind) %>%
   DT::formatRound(columns = c("estimate", "ci_lower", "ci_upper"), digits=3)

#graph
graph_yoy_male_bind <- event_study_graph_bind_main(data = estimates_yoy_male_bind, 
                                             graph_title = "Male, YOY")

ggplotly(graph_yoy_male_bind)

```

## Y = male unemployment benefit recipients(YOY)/男性の雇用保険受給者数(対前年同期差) with covar

```{r}
#merge and label estimates data
estimates_yoy_male_bind <- dplyr::bind_rows(estimates_yoy_male_OLS_notrend_covar, 
                                            estimates_yoy_male_WLS_notrend_covar, 
                                            estimates_yoy_male_OLS_trend_covar,
                                            estimates_yoy_male_WLS_trend_covar)

#change labels and reorder labels
estimates_yoy_male_bind <- estimates_labeling_main(estimates_yoy_male_bind)


# display results
DT::datatable(estimates_yoy_male_bind) %>%
   DT::formatRound(columns = c("estimate", "ci_lower", "ci_upper"), digits=3)

#graph
graph_yoy_male_bind_covar <- event_study_graph_bind_main(data = estimates_yoy_male_bind, 
                                             graph_title = "Male, YOY, with covar")

ggplotly(graph_yoy_male_bind_covar)

```

## GGplotly
```{r}
#ggplotly
ggplotly(graph_yoy_total_bind)
ggplotly(graph_yoy_total_bind_covar)
ggplotly(graph_yoy_female_bind)
ggplotly(graph_yoy_female_bind_covar)
ggplotly(graph_yoy_male_bind) 
ggplotly(graph_yoy_male_bind_covar)
```

# Merge graphs/グラフ統合
## Extract legend/legend取り出し

```{r}
#Legendの表示
graph_for_legend  <-　graph_total_bind +
 theme(legend.position = 'bottom', # Adjust x axis label
       legend.title = element_text(colour = "black", size = 20),
       legend.text = element_text(color = "black", size = 20))
graph_for_legend  

#extract legend
legend_model_types <- ggpubr::get_legend(graph_for_legend)
legend_model_types <- ggpubr::as_ggplot(legend_model_types)
legend_model_types


#２行Legendの表示
graph_for_legend_2row  <-　graph_total_bind +
 theme(legend.position = 'bottom', # Adjust x axis label
       legend.title = element_text(colour = "black", size = 20),
       legend.text = element_text(color = "black", size = 20))+
  guides(color = guide_legend(nrow = 2, byrow = TRUE)) #legendを二行に変更　2021Sep7 Waki 
graph_for_legend_2row  

#extract legend
legend_2row_model_types <- ggpubr::get_legend(graph_for_legend_2row)
legend_2row_model_types <- ggpubr::as_ggplot(legend_2row_model_types)
legend_2row_model_types


```

## Merge/統合

グラフを統合して論文用に保存。
### graph size

```{r}
dpi_num <- 100
width_num <- 15
height_num <- 18
```

### WLS with linear trends
```{r, fig.width = 10, fig.height = 15}

ymin <- - 30
ymax <- 75

ymin_num <- - 25
ymax_num  <- 75
interval <- 25

graph_total_WLS_trend <- graph_total_WLS_trend + 
  labs(title = "(a) Total unemployment benefit recipients") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_total_WLS_trend_covar <- graph_total_WLS_trend_covar + 
  labs(title = "(b) Total unemployment benefit recipients, with covaraites") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_female_WLS_trend <- graph_female_WLS_trend + 
  labs(title = "(c) Female unemployment benefit recipients")+ 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_female_WLS_trend_covar <- graph_female_WLS_trend_covar + 
  labs(title = "(d) Female unemployment benefit recipients, with covaraites") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_male_WLS_trend <- graph_male_WLS_trend + 
  labs(title = "(e) Male unemployment benefit recipients")+ 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_male_WLS_trend_covar <- graph_male_WLS_trend_covar + 
  labs(title = "(f) Male unemployment benefit recipients, with covaraites") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph <- (graph_total_WLS_trend | graph_total_WLS_trend_covar) / 
  (graph_female_WLS_trend | graph_female_WLS_trend_covar) / 
  (graph_male_WLS_trend | graph_male_WLS_trend_covar)

graph

#保存
ggsave(file = "output/graph_job_seeker_total_shock_on_UIbenefit_WLStrends.pdf", plot = graph, 
       dpi = dpi_num, width = width_num, height = height_num)  
```

### WLS with linear trends (YOY)
```{r, fig.width = 10, fig.height = 15}

ymin <- - 30
ymax <- 100

ymin_num <- - 25
ymax_num  <- 100
interval <- 25

graph_yoy_total_WLS_trend <- graph_yoy_total_WLS_trend + 
  labs(title = "(a) Total") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_yoy_total_WLS_trend_covar <- graph_yoy_total_WLS_trend_covar + 
  labs(title = "(b) Total, with covaraites") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_yoy_female_WLS_trend <- graph_yoy_female_WLS_trend + 
  labs(title = "(c) Female") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_yoy_female_WLS_trend_covar <- graph_yoy_female_WLS_trend_covar + 
  labs(title = "(d) Female, with covaraites") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_yoy_male_WLS_trend <- graph_yoy_male_WLS_trend + 
  labs(title = "(e) Male") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_yoy_male_WLS_trend_covar <- graph_yoy_male_WLS_trend_covar + 
  labs(title = "(f) Male, with covaraites") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph <- (graph_yoy_total_WLS_trend | graph_yoy_total_WLS_trend_covar) / 
  (graph_yoy_female_WLS_trend | graph_yoy_female_WLS_trend_covar) / 
  (graph_yoy_male_WLS_trend | graph_yoy_male_WLS_trend_covar)

graph

#保存
ggsave(file = "output/graph_job_seeker_total_shock_on_yoy_UIbenefit_WLStrends.pdf", plot = graph, 
       dpi = dpi_num, width = width_num, height = height_num)  
```

### Robustness check
```{r, fig.width = 10, fig.height = 15}

ymin <- - 200
ymax <- 300

ymin_num <- - 200
ymax_num  <- 300
interval <- 50

graph_total_bind <- graph_total_bind + 
  labs(title = "(a) Total unemployment benefit recipients") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_total_bind_covar <- graph_total_bind_covar + 
  labs(title = "(b) Total unemployment benefit recipients, with covaraites") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))


graph_female_bind <- graph_female_bind + 
  labs(title = "(c) Female unemployment benefit recipients")+ 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_female_bind_covar <- graph_female_bind_covar + 
  labs(title = "(d) Female unemployment benefit recipients, with covaraites") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_male_bind <- graph_male_bind + 
  labs(title = "(e) Male unemployment benefit recipients")+ 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_male_bind_covar <- graph_male_bind_covar + 
  labs(title = "(f) Male unemployment benefit recipients, with covaraites") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph <- (graph_total_bind | graph_total_bind_covar) / 
  (graph_female_bind| graph_female_bind_covar) / 
  (graph_male_bind| graph_male_bind_covar)/
  legend_model_types+
  plot_layout(heights = c(2, 2, 2, 0.5))  #0.3から0.5へ変更 2021Sep7 Waki

graph

#保存

ggsave(file = "output/graph_job_seeker_total_shock_on_UIbenefit_robust.pdf", plot = graph, 
       dpi = dpi_num, width = width_num, height = height_num)     
```

### Robustness check (YOY) 
```{r, fig.width = 10, fig.height = 15}

ymin <- - 330
ymax <- 400

ymin_num <- - 300
ymax_num  <- 400
interval <- 100

graph_yoy_total_bind <- graph_yoy_total_bind + 
  labs(title = "(a) Total") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_yoy_total_bind_covar <- graph_yoy_total_bind_covar + 
  labs(title = "(b) Total, with covaraites") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_yoy_female_bind <- graph_yoy_female_bind + 
  labs(title = "(c) Female") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_yoy_female_bind_covar <- graph_yoy_female_bind_covar + 
  labs(title = "(d) Female, with covaraites") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_yoy_male_bind <- graph_yoy_male_bind + 
  labs(title = "(e) Male") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_yoy_male_bind_covar <- graph_yoy_male_bind_covar + 
  labs(title = "(f) Male, with covaraites") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph <- (graph_yoy_total_bind | graph_yoy_total_bind_covar) / 
  (graph_yoy_female_bind| graph_yoy_female_bind_covar) / 
  (graph_yoy_male_bind| graph_yoy_male_bind_covar)/
  legend_model_types +
  plot_layout(heights = c(2, 2, 2, 0.5))  #0.3から0.5へ変更 2021Sep7 Waki

graph

#保存

ggsave(file = "output/graph_job_seeker_total_shock_on_yoy_UIbenefit_robust.pdf", plot = graph, 
       dpi = dpi_num, width = width_num, height = height_num) 

```

# Regression table/回帰結果表 without covar

```{r, eval=FALSE}
options("modelsummary_format_numeric_latex" = "plain")

# 列の選択 column order

# 男女合計、女性、男性、YOYのみ, monthlyhのみ

rows_MONTH <- tribble(~name, ~"(1)", ~"(2)", ~"(3)", ~"(4)", ~"(5)", ~"(6)", 
"Ref. month", "\\footnotesize{Jan.2020}", "\\footnotesize{$\\leq$Jan.2020}",  "\\footnotesize{Jan.2020}", "\\footnotesize{$\\leq$Jan.2020}","\\footnotesize{Jan.2020}", "\\footnotesize{$\\leq$Jan.2020}")

## results list
table_results_MONTH <- list()
table_results_MONTH[["(1)"]] <- results_yot_total_WLS_trend
table_results_MONTH[["(2)"]] <- results_yot_total_WLS_trend_onlypost
table_results_MONTH[["(3)"]] <- results_yot_female_WLS_trend
table_results_MONTH[["(4)"]] <- results_yot_female_WLS_trend_onlypost
table_results_MONTH[["(5)"]] <- results_yot_male_WLS_trend
table_results_MONTH[["(6)"]] <- results_yot_male_WLS_trend_onlypost

## HTML table
estimates_table_MONTH(df = table_results_MONTH,
                      rows = rows_MONTH,
                      title_words = "UIbenefit",
                      gof = gm,
                      output_style = "html") %>%
    kableExtra::add_header_above(c(" " = 1, "Total" = 2, "Female" = 2, "Male" = 2))

## Latex table
estimates_table_MONTH(df = table_results_MONTH,
                      rows = rows_MONTH,
                      gof = gm,
                      title_words = "DID estimates for suicide rates\\label{tab:DID_unemploy_on_suicide}", 
                      output_style = "latex") %>% 
  kableExtra::add_header_above(c(" " = 1, "Total" = 2, "Female" = 2, "Male" = 2)) %>%
  kableExtra::add_footnote(c("Notes: Robust standard errors are clustered at the prefecture level and the number of clusters (i.e. prefectures) is 47. The treatment variable is the COVID-19-induced employment shock, which is calculated as equation \\eqref{eq:employment_shock}. Estimates are obtained based on equation \\eqref{eq:did_model_ver2} with WLS estimation weighted by prefecture population size."),threeparttable = TRUE, notation = "none",escape = FALSE) %>% 
  kableExtra::column_spec(2:7, width = "1.5cm") %>% 
  kableExtra::save_kable("output/job_seeker_total_shock_on_UIbenefit_robust_tables.tex")
```

# Regression table/回帰結果表 with covar

```{r, eval=FALSE}
# 列の選択 column order

# 男女合計、女性、男性、YOYのみ, monthlyhのみ

rows_MONTH <- tribble(~name, ~"(1)", ~"(2)", ~"(3)", ~"(4)", ~"(5)", ~"(6)", 
"Ref. month", "\\footnotesize{Jan.2020}", "\\footnotesize{$\\leq$Jan.2020}",  "\\footnotesize{Jan.2020}", "\\footnotesize{$\\leq$Jan.2020}","\\footnotesize{Jan.2020}", "\\footnotesize{$\\leq$Jan.2020}")

## results list
table_results_MONTH <- list()
table_results_MONTH[["(1)"]] <- results_yot_total_WLS_trend_covar
table_results_MONTH[["(2)"]] <- results_yot_total_WLS_trend_covar_onlypost
table_results_MONTH[["(3)"]] <- results_yot_female_WLS_trend_covar
table_results_MONTH[["(4)"]] <- results_yot_female_WLS_trend_covar_onlypost
table_results_MONTH[["(5)"]] <- results_yot_male_WLS_trend_covar
table_results_MONTH[["(6)"]] <- results_yot_male_WLS_trend_covar_onlypost

## HTML table
estimates_table_MONTH(df = table_results_MONTH,
                      rows = rows_MONTH,
                      title_words = "UIbenefit",
                      gof = gm,
                      output_style = "html") %>%
    kableExtra::add_header_above(c(" " = 1, "Total" = 2, "Female" = 2, "Male" = 2))

## Latex table
estimates_table_MONTH(df = table_results_MONTH,
                      rows = rows_MONTH,
                      gof = gm,
                      title_words = "DID estimates for suicide rates\\label{tab:DID_unemploy_on_suicide}", 
                      output_style = "latex") %>% 
  kableExtra::add_header_above(c(" " = 1, "Total" = 2, "Female" = 2, "Male" = 2)) %>%
  kableExtra::add_footnote(c("Notes: Robust standard errors are clustered at the prefecture level and the number of clusters (i.e. prefectures) is 47. The treatment variable is the COVID-19-induced employment shock, which is calculated as equation \\eqref{eq:employment_shock}. Estimates are obtained based on equation \\eqref{eq:did_model_ver2} with WLS estimation weighted by prefecture population size."),threeparttable = TRUE, notation = "none",escape = FALSE) %>% 
  kableExtra::column_spec(2:7, width = "1.5cm") %>% 
  kableExtra::save_kable("output/job_seeker_total_shock_on_UIbenefit_robust_covar_tables.tex")
```
